Despite the advantages of the NB model, when the spike-train data is sparse, and a large number of neurons are recorded simultaneously, the accuracy of the estimated coefficients using GLMs with NB response is low [1]}. At the same time, under GLMs the estimator for the parameter of the NB distribution is derived entirely based on the sample means. Hence, if the length of the data is limited, be that either due to the length of the experiment or the need for real-time inference [2]}, [3]}, [4]}, [5]}, the estimator of the mean spike count can have a large mean square error (MSE). Therefore, when the model selected does not match the simulated data, further modifications need to be made to the model. One approach to address such modifications is to employ shrinkage, that is the use of other information to `shrink' the distance of the estimate towards the information, as a means to optimize the bias-variance trade-off. If we have subjective information about our parameters, using a Bayesian model can provide advantageous prior information.
